Forecasting of GPU Prices Using Transformer Method

Risyad Faisal Hadi; Siti Sa'adah; Didit Adytia

doi:10.32736/sisfokom.v12i1.1569

Authors

Risyad Faisal Hadi Telkom University
Siti Sa'adah Telkom University https://orcid.org/0000-0002-2248-3280
Didit Adytia Telkom University https://orcid.org/0000-0001-8097-5104

DOI:

https://doi.org/10.32736/sisfokom.v12i1.1569

Keywords:

GPU, Transformer, Forecasting, Time Series Forecasting

Abstract

GPU or VGA (graphic processing unit) is a vital component of computers and laptops, used for tasks such as rendering videos, creating game environments, and compiling large amounts of code. The price of GPU/VGA has fluctuated significantly since the start of the COVID-19 pandemic in 2020, due in part to the increased demand for GPUs for remote work and online activities. Furthermore, accurate GPU price forecasting can have broader implications beyond the computer hardware industry, with potential applications in investment decision-making, production planning, and pricing strategies for manufacturers. This research aims to forecast future GPU prices using deep learning-based time series forecasting using the Transformer model. We use daily prices of NVIDIA RTX 3090 Founder Edition as a test case. We use historical GPU prices to forecast 8, 16, and 30 days. Moreover, Transformer we compare the results of the Transformer model with two other models, RNN and LSTM. We found that to forecast 30 days; the Transformer model gets a higher coefficient of correlation (CC) of 0.8743, a lower root mean squared error (RMSE) value of 34.68, and a lower mean absolute percentage error (MAPE) of 0.82 compared to the RNN and LSTM model. These results suggest that the model is an effective and efficient method for predicting GPU prices.

Author Biographies

Risyad Faisal Hadi, Telkom University

Final Year student at School of Computing, Telkom University majoring in Computer Science.

Siti Sa'adah, Telkom University

Lecturer at School of Computing, Telkom University.

Didit Adytia, Telkom University

Lecturer and senior researcher in School of Computing, Telkom University in Bandung. He holds a PhD degree in Mathematics. His research topics focus on (but not limited to) Fluid dynamics, wave modelling, variational modelling, numerical implementation, and machine learning.Currently he is working on phase-resolving wave model as well as phase-averaged wave model in the aspect of wave modelling and numerical implementation. He is also actively working on meteorology & (physical) oceanography (MetOcean) work, especially related with desktop study of WIND & WAVE.His research interest is a combination of deterministic modelling ('hard-computing') as well as approach with 'soft computing' (machine learning) in field of physics, oceanography, meteorology, or geosciences in general.

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